Consensus Set Research Articles

The use of the Delphi technique as part of the process of developing a tool to evaluate physiotherapy clinical education programmes


 
 
 
 Objectively evaluating the quality and effectiveness of a physiotherapy clinical education programme has been unsuccessful to date, due to its complexity and the lack of a standardised tool. We undertook to develop a standardised programme evaluation tool and used the Delphi method to obtain consensus (set at 80%) to determine the face and content validity of the items and domains of the tool, the scoring system and a name for the tool. Academics, clinical physiotherapists and clinical physiotherapy educators participated in the Delphi rounds. Three Delphi rounds ensued: in Delphi round 1, a 71% response rate was obtained and 49 questions obtained 80% consensus. In Delphi round 2, a 91% response rate was obtained and 59 questions obtained 80% consensus. In Delphi round 3, a 42% response rate was obtained, several names were suggested, and the scoring system was established. The provisional tool of 85 items ultimately emerged as the Vaneshveri Naidoo Clinical Programme Evaluation Tool (VN-CPET).
 
 
 

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm

PurposeThis study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.MethodsImages were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus—Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.ResultsA total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).ConclusionPeripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study

Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI systems trained on large datasets for practitioners to use with their own data with a view to accelerating clinical deployment of AI systems in radiology. We aimed to contribute an AI system for comprehensive chest x-ray abnormality detection. In this retrospective cohort study, we developed open-source neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest x-ray findings from images and their free-text reports. Our networks were developed using data from six UK hospitals from three National Health Service (NHS) Trusts (University Hospitals Coventry and Warwickshire NHS Trust, University Hospitals Birmingham NHS Foundation Trust, and University Hospitals Leicester NHS Trust) collectively contributing 2 513 546 chest x-ray studies taken from a 13-year period (2006-19), which yielded 1 940 508 usable free-text radiological reports written by the contemporary assessing radiologist (collectively referred to as the "historic reporters") and 1 896 034 frontal images. Chest x-rays were labelled using a taxonomy of 37 findings by a custom-trained natural language processing (NLP) algorithm, X-Raydar-NLP, from the original free-text reports. X-Raydar-NLP was trained on 23 230 manually annotated reports and tested on 4551 reports from all hospitals. 1 694 921 labelled images from the training set and 89 238 from the validation set were then used to train a multi-label image classifier. Our algorithms were evaluated on three retrospective datasets: a set of exams sampled randomly from the full NHS dataset reported during clinical practice and annotated using NLP (n=103 328); a consensus set sampled from all six hospitals annotated by three expert radiologists (two independent annotators for each image and a third consultant to facilitate disagreement resolution) under research conditions (n=1427); and an independent dataset, MIMIC-CXR, consisting of NLP-annotated exams (n=252 374). X-Raydar achieved a mean AUC of 0·919 (SD 0·039) on the auto-labelled set, 0·864 (0·102) on the consensus set, and 0·842 (0·074) on the MIMIC-CXR test, demonstrating similar performance to the historic clinical radiologist reporters, as assessed on the consensus set, for multiple clinically important findings, including pneumothorax, parenchymal opacification, and parenchymal mass or nodules. On the consensus set, X-Raydar outperformed historical reporter balanced accuracy with significance on 27 of 37 findings, was non-inferior on nine, and inferior on one finding, resulting in an average improvement of 13·3% (SD 13·1) to 0·763 (0·110), including a mean 5·6% (13·2) improvement in critical findings to 0·826 (0·119). Our study shows that automated classification of chest x-rays under a comprehensive taxonomy can achieve performance levels similar to those of historical reporters and exhibit robust generalisation to external data. The open-sourced neural networks can serve as foundation models for further research and are freely available to the research community. Wellcome Trust.

An international consensus on effective, inclusive, and career-spanning short-format training in the life sciences and beyond.

Science, technology, engineering, mathematics, and medicine (STEMM) fields change rapidly and are increasingly interdisciplinary. Commonly, STEMM practitioners use short-format training (SFT) such as workshops and short courses for upskilling and reskilling, but unaddressed challenges limit SFT's effectiveness and inclusiveness. Education researchers, students in SFT courses, and organizations have called for research and strategies that can strengthen SFT in terms of effectiveness, inclusiveness, and accessibility across multiple dimensions. This paper describes the project that resulted in a consensus set of 14 actionable recommendations to systematically strengthen SFT. A diverse international group of 30 experts in education, accessibility, and life sciences came together from 10 countries to develop recommendations that can help strengthen SFT globally. Participants, including representation from some of the largest life science training programs globally, assembled findings in the educational sciences and encompassed the experiences of several of the largest life science SFT programs. The 14 recommendations were derived through a Delphi method, where consensus was achieved in real time as the group completed a series of meetings and tasks designed to elicit specific recommendations. Recommendations cover the breadth of SFT contexts and stakeholder groups and include actions for instructors (e.g., make equity and inclusion an ethical obligation), programs (e.g., centralize infrastructure for assessment and evaluation), as well as organizations and funders (e.g., professionalize training SFT instructors; deploy SFT to counter inequity). Recommendations are aligned with a purpose-built framework-"The Bicycle Principles"-that prioritizes evidenced-based teaching, inclusiveness, and equity, as well as the ability to scale, share, and sustain SFT. We also describe how the Bicycle Principles and recommendations are consistent with educational change theories and can overcome systemic barriers to delivering consistently effective, inclusive, and career-spanning SFT.

How Do Molecular Classifications Affect the Neoadjuvant Treatment of Muscle-Invasive Urothelial Carcinoma?

Despite the significant improvements in the field of oncological treatments in recent decades, and the advent of targeted therapies and immunotherapy, urothelial carcinoma of the bladder remains a highly heterogeneous and difficult-to-treat neoplasm with a poor prognosis. In this context, owing to the new methods of genomic sequencing, numerous studies have analyzed the genetic features of muscle-invasive bladder cancer, providing a consensus set of molecular classes, to identify malignancies that may respond better to specific treatments (standard chemotherapy, immunotherapy, target therapy, local-regional treatment, or combinations) and improve the survival. The aim of the current review is to provide an overview of the current status of the molecular landscape of muscle-invasive bladder cancer, focusing our attention on therapeutic and prognostic implications in order to select the most effective and tailored therapeutic regimen for the individual patient.

SC 2-PCR++: Rethinking the Generation and Selection for Efficient and Robust Point Cloud Registration.

Outlier removal is a critical part of feature-based point cloud registration. In this article, we revisit the model generation and selection of the classic RANSAC approach for fast and robust point cloud registration. For the model generation, we propose a second-order spatial compatibility (SC 2) measure to compute the similarity between correspondences. It takes into account global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at an early stage. The proposed measure promises to find a certain number of outlier-free consensus sets using fewer samplings, making the model generation more efficient. For the model selection, we propose a new Feature and Spatial consistency constrained Truncated Chamfer Distance (FS-TCD) metric for evaluating the generated models. It considers the alignment quality, the feature matching properness, and the spatial consistency constraint simultaneously, enabling the correct model to be selected even when the inlier rate of the putative correspondence set is extremely low. Extensive experiments are carried out to investigate the performance of our method. In addition, we also experimentally prove that the proposed SC 2 measure and the FS-TCD metric are general and can be easily plugged into deep learning based frameworks.

Validation of an Educational Tool for Skin Abscess Incision and Drainage by Delphi and Angoff Methods.

Bedside incision and drainage (I&D) of skin abscesses is a common medical procedure performed in a variety of medical settings. Yet, there is a paucity of published validated educational tools to teach and assess competency for this procedure. To validate an educational tool to teach and assess competency for bedside I&D of skin abscesses via the Delphi consensus and Angoff standard setting methods. Expert consensus on the importance of each procedural step in the educational tool was obtained using the Delphi method, consisting of four rounds of iterative revisions based on input from a panel of experts. The passing cut-off score for a proficient provider was determined using the modified dichotomous Angoff method. All participants met the minimum criteria of active involvement in resident education and performance of at least 20 skin abscess I&D's within the past 5years. Participant specialties included general surgery, emergency medicine, and internal medicine. The primary outcome was consensus on procedural steps and errors, defined as an interquartile range ≤ 2 on a 9-point Likert scale. A cut-off score was determined by the average across all respondents for the anticipated number of errors that would be committed by a provider with the level of proficiency defined in the survey. Qualitative input was incorporated into the educational tool. At the end of four rounds of review via the Delphi process, participants achieved consensus on 93% of items on the clinical checklist and 85% of errors on the assessment checklist. Via the modified dichotomous Angoff method, the determined passing cut-off for competency was 6 out of 22 errors. An educational and evaluation tool for bedside I&D of skin abscesses was validated via the Delphi and Angoff methods.

An improved PBFT consensus algorithm based on grouping and credit grading

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.

IROCA-TES: Improving Quality in Radiation Oncology through Clinical Audits - Training and Education for Standardization.

Clinical audits are an important tool to objectively assess clinical protocols, procedures, and processes and to detect deviations from good clinical practice. The main aim of this project is to determine adherence to a core set of consensus- based quality indicators and then to compare the institutions in order to identify best practices. We conduct a multicentre, international clinical audit of six comprehensive cancer centres in Poland, Spain, Italy, Portugal, France, and Romania as a part of the project, known as IROCATES (Improving Quality in Radiation Oncology through Clinical Audits - Training and Education for Standardization). Radiotherapy practice varies from country to country, in part due to historical, economic, linguistic, and cultural differences. The institutions developed their own processes to suit their existing clinical practice. We believe that this study will contribute to establishing the value of routinely performing multi-institutional clinical audits and will lead to improvement of radiotherapy practice at the participating centres.

Minimum data elements for the Australian Particle Therapy Clinical Quality Registry.

Construction of the first Australian particle therapy (PT) centre is underway. Establishment of a national registry, to be known as the Australian Particle Therapy Clinical Quality Registry (ASPIRE), has been identified as a mandatory requirement for PT treatment to be reimbursed by the Australian Medicare Benefits Schedule. This study aimed to determine a consensus set of Minimum Data Elements (MDEs) for ASPIRE. A modified Delphi and expert consensus process was completed. Stage 1 compiled currently operational English-language international PT registries. Stage 2 listed the MDEs included in each of these four registries. Those included in three or four registries were automatically included as a potential MDE for ASPIRE. Stage 3 interrogated the remaining data items, and involved three rounds - an online survey to a panel of experts, followed by a live poll session of PT-interested participants, and finally a virtual discussion forum of the original expert panel. One hundred and twenty-three different MDEs were identified across the four international registries. The multi-staged Delphi and expert consensus process resulted in a total of 27 essential MDEs for ASPIRE; 14 patient factors, four tumour factors and nine treatment factors. The MDEs provide the core mandatory data items for the national PT registry. Registry data collection for PT is paramount in the ongoing global effort to accumulate more robust clinical evidence regarding PT patient and tumour outcomes, quantifying the magnitude of clinical benefit and justifying the relatively higher costs of PT investment.

Iterative Update Sample Consensus (IUSAC): A repeatable algorithm for optimal consensus set

The RANdom SAmple Consensus (RANSAC) is one of the most powerful tools for the reconstruction of ground structures from point cloud observations in many applications. The algorithm utilizes iterative search techniques for a set of inliers to find a proper model for the given data. Despite its successful performances in many fields, the RANSAC is not repeatable, i.e, the optimal solution is not guaranteed by its repeated run. In this article, we propose a new algorithm for the reconstruction, called the Iterative Update SAmple Consensus (IUSAC), which is repeatable to get the optimal estimation for the model parameters. The main idea behind the IUSAC is the iterative update, which makes the estimated parameters converge to the optimal solution. The proposed algorithm is analyzed for convergence. It has been numerically verified that the IUSAC is repeatable, simple to implement, and more efficient than the RANSAC. Various numerical experiments incorporating synthetic datasets and the KITTI dataset validate the performance of the IUSAC. The KITTI dataset is a public domain dataset to serve for R&D activities on autonomous vehicle tracking in GPS-denied environments.

The Transcriptomic Signature of Cyclical Parthenogenesis.

Cyclical parthenogenesis, where females can engage in sexual or asexual reproduction depending on environmental conditions, represents a novel reproductive phenotype that emerged during eukaryotic evolution. The fact that environmental conditions can trigger cyclical parthenogens to engage in distinct reproductive modes strongly suggests that gene expression plays a key role in the origin of cyclical parthenogenesis. However, the genetic basis underlying cyclical parthenogenesis remains understudied. In this study, we characterize the female transcriptomic signature of sexual versus asexual reproduction in the cyclically parthenogenetic microcrustacean Daphnia pulex and Daphnia pulicaria. Our analyses of differentially expressed genes (DEGs), pathway enrichment, and gene ontology (GO) term enrichment clearly show that compared with sexual reproduction, the asexual reproductive stage is characterized by both the underregulation of meiosis and cell cycle genes and the upregulation of metabolic genes. The consensus set of DEGs that this study identifies within the meiotic, cell cycle, and metabolic pathways serves as candidate genes for future studies investigating how the two reproductive cycles in cyclical parthenogenesis are mediated at a molecular level. Furthermore, our analyses identify some cases of divergent expression among gene family members (e.g., doublesex and NOTCH2) associated with asexual or sexual reproductive stage, suggesting potential functional divergence among gene family members.

How specific combinations of epoch length, non-wear time and cut-points influence physical activity

This study assesses three factors that influence the quantification of children’s and adolescents’ physical activity (PA) using accelerometers: selection of (1) non-wear algorithm, (2) epoch length and (3) cut-points. A total of 1525 participants from MoMo wave 3 (2018–2022), aged 6–17 years, wore GT3X accelerometers (ActiGraph, LLC, Pensacola, FL, USA) during waking hours. Acceleration counts were reintegrated into lengths of 1, 5, 15, 30, and 60 s epochs. Two non-wear time algorithms and two sets of cut-points were applied to each epoch length. Differences were found in both the comparison of the non-wear time algorithms and the comparison of the cut-points when the different epoch lengths were considered. This may result in large differences in estimated sedentary behavior and PA values. We propose to pool the data by merging and combining multiple accelerometer datasets from different studies and evaluate them in a harmonized way in the future. In addition to the need for future validation studies using short epoch lengths for young children, we also propose to conduct meta-analyses. This allows the use of data from multiple studies to validate cut-points and to propose a consensual set of cut-points that can be used in different settings and projects. The high discrepancy between results when comparing different epoch lengths has to be considered when interpreting accelerometer data and is regarded a confounding variable when comparing levels of PA between studies.

How to reach agreement: the impact of different analytical approaches to Delphi process results in core outcomes set development

BackgroundCore outcomes sets are increasingly used to define research outcomes that are most important for a condition. Different consensus methods are used in the development of core outcomes sets; the most common is the Delphi process. Delphi methodology is increasingly standardised for core outcomes set development, but uncertainties remain. We aimed to empirically test how the use of different summary statistics and consensus criteria impact Delphi process results.MethodsResults from two unrelated child health Delphi processes were analysed. Outcomes were ranked by mean, median, or rate of exceedance, and then pairwise comparisons were undertaken to analyse whether the rankings were similar. The correlation coefficient for each comparison was calculated, and Bland-Altman plots produced. Youden’s index was used to assess how well the outcomes ranked highest by each summary statistic matched the final core outcomes sets.Consensus criteria identified in a review of published Delphi processes were applied to the results of the two child-health Delphi processes. The size of the consensus sets produced by different criteria was compared, and Youden’s index was used to assess how well the outcomes that met different criteria matched the final core outcomes sets.ResultsPairwise comparisons of different summary statistics produced similar correlation coefficients. Bland–Altman plots showed that comparisons involving ranked medians had wider variation in the ranking. No difference in Youden’s index for the summary statistics was found.Different consensus criteria produced widely different sets of consensus outcomes (range: 5–44 included outcomes). They also showed differing abilities to identify core outcomes (Youden’s index range: 0.32–0.92). The choice of consensus criteria had a large impact on Delphi results.DiscussionThe use of different summary statistics is unlikely to affect how outcomes are ranked during a Delphi process: mean, median, and rates of exceedance produce similar results. Different consensus criteria have a large impact on resultant consensus outcomes and potentially on subsequent core outcomes sets: our results confirm the importance of adhering to pre-specified consensus criteria.

The development of large public infrastructure projects: integrating policy and project studies models

AbstractProject management theory often reduces development to a simplistic and smooth process of consultation leading to a consensual set of requirements. However, in large public infrastructure projects, this is rarely the case as development is often subject to major power struggles. This article shows that public policy theory has an excellent potential to shed a fresh light on project development. An integrated model combining the theoretical insights from the Advocacy Coalition Framework and project development studies is presented and illustrated using the case of a major Canadian city streetcar network megaproject. The implications of the model for understanding “wicked problems” are discussed.

Expert consensus on monitoring antimicrobial stewardship in French nursing homes using assessed reimbursement database indicators.

Monitoring the appropriateness of antibiotic prescriptions with indicators based on reimbursement data is required to guide antibiotic stewardship (AMS) interventions in nursing homes (NHs). Quantity metrics (QMs) monitor the volume of prescriptions while proxy indicators (PIs) reflect the appropriateness of antibiotic use. Our objectives were: (i) to provide a relevant consensual set of indicators to be used in French NHs; and (ii) to assess the feasibility of their implementation at the national and local scale. Nine French professional organizations implicated in AMS in NHs were asked to nominate at least one member to create a national expert panel of 20 physicians. Twenty-one recently published QMs and 11 PIs were assessed by the expert panel. Indicators were evaluated using a RAND-modified Delphi procedure comprising two online surveys and a videoconference meeting. Indicators were kept in the final list if >70% of stakeholders validated their relevance for estimating the volume (QMs) and appropriateness (PIs) of prescriptions. Of the 21 QM indicators submitted to the panel, 14 were selected, describing the consumption of antibiotics overall (n = 3), broad-spectrum (n = 6) and second-line antibiotics (n = 2). The three remaining QMs evaluated the route of administration (n = 1) and urine culture prescriptions (n = 2). Ten PIs (six modified, two rejected, one new) were selected to assess the appropriateness of prescriptions for urinary tract infections (n = 2), seasonal variations in prescriptions (n = 2), repeated prescriptions of fluoroquinolones (n = 1), cephalosporins' route of administration (n = 1), duration of treatment (n = 1), rate of second-line antibiotics (n = 1), co-prescriptions with non-steroidal anti-inflammatory drugs (n = 1), and flu vaccine coverage (n = 1). The panel was in favour of using these indicators for regional and facility level AMS programmes (91%), feedback to NH prescribers (82%), benchmarking by health authorities (55%) and public reporting at the facility level (9%). This consensual list of indicators, covering a wide range of frequent clinical situations, may be used as part of the French national AMS strategy for monitoring antibiotic prescriptions in NHs at the national and local levels. Regional AMS networks might manage this selected list to guide personalized action plans with concrete objectives of reducing the quantity and improving the quality of antibiotic prescriptions.

Clinical recommendations on penile reconstructive and prosthetic surgery: a consensus statement from the Asia-Pacific Society of Sexual Medicine.

Penile reconstructive and prosthetic surgery remains a highly specialized field where potential complications can be devastating, and unrealistic patient expectations can often be difficult to manage. Furthermore, surgical practice can vary depending on locoregional expertise and sociocultural factors. The Asia Pacific Society of Sexual Medicine (APSSM) panel of experts reviewed contemporary evidence regarding penile reconstructive and prosthetic surgery with an emphasis on key issues relevant to the Asia-Pacific (AP) region and developed a consensus statement and set of clinical practice recommendations on behalf of the APSSM. The Medline and EMBASE databases were searched using the following terms: "penile prosthesis implant," "Peyronie's disease," "penile lengthening," "penile augmentation," "penile enlargement," "buried penis," "penile disorders," "penile trauma," "transgender," and "penile reconstruction" between January 2001 and June 2022. A modified Delphi method was undertaken, and the panel evaluated, agreed, and provided consensus statements on clinically relevant penile reconstructive and prosthetic surgery, namely (1) penile prosthesis implantation, (2) Peyronie's disease, (3) penile trauma, (4) gender-affirming (phalloplasty) surgery, and (5) penile esthetic (length and/or girth enlargement) surgery. Outcomes were specific statements and clinical recommendations according to the Oxford Centre for Evidence-Based Medicine, and if clinical evidence is lacking, a consensus agreement is adopted. The panel provided statements on clinical aspects of surgical management in penile reconstructive and prosthetic surgery. There is a variation in surgical algorithms in patients based on sociocultural characteristics and the availability of local resources. Performing preoperative counseling and obtaining adequate informed consent are paramount and should be conducted to discuss various treatment options, including the pros and cons of each surgical intervention. Patients should be provided with information regarding potential complications related to surgery, and strict adherence to safe surgical principles, preoperative optimization of medical comorbidities and stringent postoperative care are important to improve patient satisfaction rates. For complex patients, surgical intervention should ideally be referred and performed by expert high-volume surgeons to maximize clinical outcomes. Due to the uneven distribution of surgical access and expertise across the AP region, development of relevant comprehensive surgical protocols and regular training programs is desirable. This consensus statement covers comprehensive penile reconstructive and prosthetic surgery topics and is endorsed by the APSSM. The variations in surgical algorithms and lack of sufficient high-level evidence in these areas could be stated as a limitation. This APSSM consensus statement provides clinical recommendations on the surgical management of various penile reconstructive and prosthetic surgeries. The APSSM advocates for surgeons in AP to individualize surgical options based on patient condition(s) and needs, surgeon expertise, and local resources.

Consensus-based recommendations on physical activity and exercise in patients with diabetes at risk of foot ulcerations: a Delphi study

BackgroundPatients with diabetes mellitus are exposed to important complications. Currently little evidence exist on the guidelines that these patients, at some risk of foot ulceration, should follow for physical exercise. ObjectivesTo reach a consensus among multidisciplinary and international experts on physical activity/exercise recommendations for patients with diabetes, according to foot ulcer risk. MethodsUsing a three-round Delphi method, a panel composed of 28 multidisciplinary experts in the management of diabetic foot assessed 109 recommendations on physical activity/exercise for patients with diabetes mellitus, according to their risk of foot ulcer. Consensus was assumed when 80% of responses matched the same category (agreement/disagreement). ResultsTwenty-nine experts participated in the first and second rounds of consultation, and twenty-eight did so in the third, reaching final agreement on 86 of the 109 recommendations considered (78.9%). The study, thus, generated a consensus set of recommendations concerning different aspects of diabetic footcare before, during, and after exercise (e.g. when to examine the foot, how to assess it, what type of sock and insole to use, what types of exercise to perform, and when it is advisable to return to activity after an ulceration). ConclusionThis Delphi study generated recommendations based on the consensus of international experts on physical activity and exercise by patient with diabetes at risk of ulceration. Recommendations considered the state of the foot and the patient's history and status before physical activity and included information on intensity, duration, frequency, and progressions of physical activity/exercise, and the use of custom-made plantar orthoses, shoe prescription, and the convenience of returning to physical activity after an ulceration.

Detection and characterization of constitutive replication origins defined by DNA polymerase epsilon

BackgroundDespite the process of DNA replication being mechanistically highly conserved, the location of origins of replication (ORI) may vary from one tissue to the next, or between rounds of replication in eukaryotes, suggesting flexibility in the choice of locations to initiate replication. Lists of human ORI therefore vary widely in number and location, and there are currently no methods available to compare them. Here, we propose a method of detection of ORI based on somatic mutation patterns generated by the mutator phenotype of damaged DNA polymerase epsilon (POLE).ResultsWe report the genome-wide localization of constitutive ORI in POLE-mutated human tumors using whole genome sequencing data. Mutations accumulated after many rounds of replication of unsynchronized dividing cell populations in tumors allow to identify constitutive origins, which we show are shared with high fidelity between individuals and tumor types. Using a Smith–Waterman-like dynamic programming approach, we compared replication origin positions obtained from multiple different methods. The comparison allowed us to define a consensus set of replication origins, identified consistently by multiple ORI detection methods. Many DNA features co-localized with the consensus set of ORI, including chromatin loop anchors, G-quadruplexes, S/MARs, and CpGs. Among all features, the H2A.Z histone exhibited the most significant association.ConclusionsOur results show that mutation-based detection of replication origins is a viable approach to determining their location and associated sequence features.

A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study

Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.